Added new documentation folders for v0.0.4
This commit is contained in:
Eduardo Cueto Mendoza
parent
278af852f4
commit
6021828772
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@ -2,3 +2,4 @@
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*.jl.cov
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*.jl.cov
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*.jl.mem
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*.jl.mem
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/docs/build/
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/docs/build/
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.vscode/*
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410
Manifest.toml
410
Manifest.toml
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@ -1,2 +1,412 @@
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# This file is machine-generated - editing it directly is not advised
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# This file is machine-generated - editing it directly is not advised
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[[AbstractFFTs]]
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deps = ["LinearAlgebra"]
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git-tree-sha1 = "051c95d6836228d120f5f4b984dd5aba1624f716"
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||||||
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uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
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||||||
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version = "0.5.0"
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||||||
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[[AbstractTrees]]
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deps = ["Markdown"]
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||||||
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git-tree-sha1 = "33e450545eaf7699da1a6e755f9ea65f14077a45"
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||||||
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uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
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version = "0.3.3"
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[[Adapt]]
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deps = ["LinearAlgebra"]
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git-tree-sha1 = "fd04049c7dd78cfef0b06cdc1f0f181467655712"
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uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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version = "1.1.0"
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[[ArrayLayouts]]
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deps = ["FillArrays", "LinearAlgebra"]
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git-tree-sha1 = "a3254b3780a3544838ca0b7e23b1e9b06eb71bd8"
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uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
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version = "0.3.5"
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[[Base64]]
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uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
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[[BinaryProvider]]
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deps = ["Libdl", "Logging", "SHA"]
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git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
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uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
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version = "0.5.10"
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[[CEnum]]
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git-tree-sha1 = "1b77a77c3b28e0b3f413f7567c9bb8dd9bdccd14"
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uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
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version = "0.3.0"
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[[CUDAapi]]
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deps = ["Libdl", "Logging"]
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git-tree-sha1 = "831b825d10104bd29e28f6da93312a976830717b"
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pinned = true
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uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
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version = "4.0.0"
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[[CUDAdrv]]
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deps = ["CEnum", "CUDAapi", "Printf"]
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git-tree-sha1 = "f56bbf18c86bcff7a961a32a4947a5abb2963a29"
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uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
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version = "6.3.0"
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[[CUDAnative]]
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deps = ["Adapt", "BinaryProvider", "CEnum", "CUDAapi", "CUDAdrv", "ExprTools", "GPUCompiler", "LLVM", "Libdl", "Pkg", "Printf"]
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git-tree-sha1 = "ac86db2b05fdfec96b011e25a504ffe7476e8a68"
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uuid = "be33ccc6-a3ff-5ff2-a52e-74243cff1e17"
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version = "3.1.0"
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[[ChainRules]]
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deps = ["ChainRulesCore", "LinearAlgebra", "Reexport", "Requires", "Statistics"]
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git-tree-sha1 = "76cd719cb7ab57bd2687dcb3b186c4f99820a79d"
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uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2"
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version = "0.6.5"
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[[ChainRulesCore]]
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deps = ["MuladdMacro"]
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git-tree-sha1 = "c384e0e4fe6bfeb6bec0d41f71cc5e391cd110ba"
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uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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version = "0.8.1"
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[[CodeTracking]]
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deps = ["InteractiveUtils", "UUIDs"]
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git-tree-sha1 = "9c173f62af93cce8af2bd3527d160b6ddd6eaf81"
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uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2"
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version = "1.0.0"
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[[CodecZlib]]
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deps = ["TranscodingStreams", "Zlib_jll"]
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git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
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uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
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version = "0.7.0"
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[[ColorTypes]]
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deps = ["FixedPointNumbers", "Random"]
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git-tree-sha1 = "27eb374570946a02aa184ef5b403dabaa7380693"
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uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f"
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version = "0.10.4"
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[[Colors]]
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deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"]
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git-tree-sha1 = "1e9bba7984e78aa8cdeea7f9f7cc984ad4e4b1c7"
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uuid = "5ae59095-9a9b-59fe-a467-6f913c188581"
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version = "0.12.2"
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[[CommonSubexpressions]]
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deps = ["Test"]
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git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0"
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uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
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version = "0.2.0"
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[[CompilerSupportLibraries_jll]]
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deps = ["Libdl", "Pkg"]
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git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
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uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
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version = "0.3.3+0"
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[[Cthulhu]]
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deps = ["CodeTracking", "FoldingTrees", "InteractiveUtils", "REPL", "UUIDs", "Unicode"]
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git-tree-sha1 = "34bf82a46f0600727673e3cbed9a5908efff3c0c"
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uuid = "f68482b8-f384-11e8-15f7-abe071a5a75f"
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version = "1.2.0"
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[[CuArrays]]
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deps = ["AbstractFFTs", "Adapt", "CEnum", "CUDAapi", "CUDAdrv", "CUDAnative", "DataStructures", "GPUArrays", "Libdl", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SparseArrays", "Statistics", "TimerOutputs"]
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git-tree-sha1 = "55ab24cc3fcf96f74246cf7e14bc9c2f818facbc"
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uuid = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
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version = "2.2.2"
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[[DataAPI]]
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git-tree-sha1 = "176e23402d80e7743fc26c19c681bfb11246af32"
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uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
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version = "1.3.0"
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[[DataStructures]]
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deps = ["InteractiveUtils", "OrderedCollections"]
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git-tree-sha1 = "edad9434967fdc0a2631a65d902228400642120c"
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uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
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version = "0.17.19"
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[[Dates]]
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deps = ["Printf"]
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uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
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[[DelimitedFiles]]
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||||||
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deps = ["Mmap"]
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||||||
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uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab"
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[[DiffResults]]
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deps = ["StaticArrays"]
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||||||
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git-tree-sha1 = "da24935df8e0c6cf28de340b958f6aac88eaa0cc"
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uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
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||||||
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version = "1.0.2"
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[[DiffRules]]
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||||||
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deps = ["NaNMath", "Random", "SpecialFunctions"]
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||||||
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git-tree-sha1 = "eb0c34204c8410888844ada5359ac8b96292cfd1"
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uuid = "b552c78f-8df3-52c6-915a-8e097449b14b"
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version = "1.0.1"
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[[Distributed]]
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||||||
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deps = ["Random", "Serialization", "Sockets"]
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||||||
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uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
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[[ExprTools]]
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git-tree-sha1 = "6f0517056812fd6aa3af23d4b70d5325a2ae4e95"
|
||||||
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uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
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||||||
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version = "0.1.1"
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|
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[[FillArrays]]
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||||||
|
deps = ["LinearAlgebra", "Random", "SparseArrays"]
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||||||
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git-tree-sha1 = "bf726ba7ce99e00d10bf63c031285fb9ab3676ae"
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||||||
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uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
|
||||||
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version = "0.8.11"
|
||||||
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|
||||||
|
[[FixedPointNumbers]]
|
||||||
|
git-tree-sha1 = "8fb797c37a3b7ced4327a05ac4ca0dd6a4f1ba92"
|
||||||
|
uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
|
||||||
|
version = "0.8.1"
|
||||||
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|
||||||
|
[[Flux]]
|
||||||
|
deps = ["AbstractTrees", "Adapt", "CodecZlib", "Colors", "CuArrays", "DelimitedFiles", "Juno", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "SHA", "Statistics", "StatsBase", "Test", "ZipFile", "Zygote"]
|
||||||
|
git-tree-sha1 = "eb5801eea6294851dc2b16b20669f91776e79a3b"
|
||||||
|
pinned = true
|
||||||
|
uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c"
|
||||||
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version = "0.10.4"
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[[FoldingTrees]]
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deps = ["AbstractTrees", "REPL"]
|
||||||
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git-tree-sha1 = "9a1e497cba7c33e5951aaffd9a421fa713acba0b"
|
||||||
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uuid = "1eca21be-9b9b-4ed8-839a-6d8ae26b1781"
|
||||||
|
version = "1.0.0"
|
||||||
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|
||||||
|
[[ForwardDiff]]
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||||||
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deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "NaNMath", "Random", "SpecialFunctions", "StaticArrays"]
|
||||||
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git-tree-sha1 = "869540e4367122fbffaace383a5bdc34d6e5e5ac"
|
||||||
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uuid = "f6369f11-7733-5829-9624-2563aa707210"
|
||||||
|
version = "0.10.10"
|
||||||
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|
||||||
|
[[Future]]
|
||||||
|
deps = ["Random"]
|
||||||
|
uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
|
||||||
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|
||||||
|
[[GPUArrays]]
|
||||||
|
deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"]
|
||||||
|
git-tree-sha1 = "d887693eb1bd5e1fd573262a978745481895ec7d"
|
||||||
|
uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
|
||||||
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version = "3.4.1"
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|
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[[GPUCompiler]]
|
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deps = ["Cthulhu", "DataStructures", "InteractiveUtils", "LLVM", "Libdl", "TimerOutputs"]
|
||||||
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git-tree-sha1 = "5275aa268ecd09640b32560e1eae90c78816e4d1"
|
||||||
|
uuid = "61eb1bfa-7361-4325-ad38-22787b887f55"
|
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version = "0.2.0"
|
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|
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[[IRTools]]
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||||||
|
deps = ["InteractiveUtils", "MacroTools", "Test"]
|
||||||
|
git-tree-sha1 = "6875ae3cfcb9a50af80553d5cc825f406e8d13bc"
|
||||||
|
uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
|
||||||
|
version = "0.4.0"
|
||||||
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|
||||||
|
[[InteractiveUtils]]
|
||||||
|
deps = ["Markdown"]
|
||||||
|
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
|
||||||
|
|
||||||
|
[[Juno]]
|
||||||
|
deps = ["Base64", "Logging", "Media", "Profile"]
|
||||||
|
git-tree-sha1 = "a686b0cf235fa3e491b79b4783c2d2382292b436"
|
||||||
|
uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
|
||||||
|
version = "0.8.2"
|
||||||
|
|
||||||
|
[[LLVM]]
|
||||||
|
deps = ["CEnum", "Libdl", "Printf", "Unicode"]
|
||||||
|
git-tree-sha1 = "d9c6e1efcaa6c2fcd043da812a62b3e489a109a3"
|
||||||
|
uuid = "929cbde3-209d-540e-8aea-75f648917ca0"
|
||||||
|
version = "1.7.0"
|
||||||
|
|
||||||
|
[[LibGit2]]
|
||||||
|
deps = ["Printf"]
|
||||||
|
uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
|
||||||
|
|
||||||
|
[[Libdl]]
|
||||||
|
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
|
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|
|
||||||
|
[[LinearAlgebra]]
|
||||||
|
deps = ["Libdl"]
|
||||||
|
uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
|
||||||
|
|
||||||
|
[[Logging]]
|
||||||
|
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
|
||||||
|
|
||||||
|
[[MacroTools]]
|
||||||
|
deps = ["Markdown", "Random"]
|
||||||
|
git-tree-sha1 = "f7d2e3f654af75f01ec49be82c231c382214223a"
|
||||||
|
uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
|
||||||
|
version = "0.5.5"
|
||||||
|
|
||||||
|
[[Markdown]]
|
||||||
|
deps = ["Base64"]
|
||||||
|
uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
|
||||||
|
|
||||||
|
[[Media]]
|
||||||
|
deps = ["MacroTools", "Test"]
|
||||||
|
git-tree-sha1 = "75a54abd10709c01f1b86b84ec225d26e840ed58"
|
||||||
|
uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27"
|
||||||
|
version = "0.5.0"
|
||||||
|
|
||||||
|
[[Missings]]
|
||||||
|
deps = ["DataAPI"]
|
||||||
|
git-tree-sha1 = "de0a5ce9e5289f27df672ffabef4d1e5861247d5"
|
||||||
|
uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
|
||||||
|
version = "0.4.3"
|
||||||
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|
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|
[[Mmap]]
|
||||||
|
uuid = "a63ad114-7e13-5084-954f-fe012c677804"
|
||||||
|
|
||||||
|
[[MuladdMacro]]
|
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git-tree-sha1 = "c6190f9a7fc5d9d5915ab29f2134421b12d24a68"
|
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uuid = "46d2c3a1-f734-5fdb-9937-b9b9aeba4221"
|
||||||
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version = "0.2.2"
|
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[[NNlib]]
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||||||
|
deps = ["BinaryProvider", "Libdl", "LinearAlgebra", "Requires", "Statistics"]
|
||||||
|
git-tree-sha1 = "d9f196d911f55aeaff11b11f681b135980783824"
|
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|
uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
|
||||||
|
version = "0.6.6"
|
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[[NaNMath]]
|
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|
git-tree-sha1 = "928b8ca9b2791081dc71a51c55347c27c618760f"
|
||||||
|
uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
|
||||||
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version = "0.3.3"
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[[OpenSpecFun_jll]]
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deps = ["CompilerSupportLibraries_jll", "Libdl", "Pkg"]
|
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|
git-tree-sha1 = "d51c416559217d974a1113522d5919235ae67a87"
|
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|
uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
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version = "0.5.3+3"
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[[OrderedCollections]]
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||||||
|
git-tree-sha1 = "12ce190210d278e12644bcadf5b21cbdcf225cd3"
|
||||||
|
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
|
||||||
|
version = "1.2.0"
|
||||||
|
|
||||||
|
[[Pkg]]
|
||||||
|
deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
|
||||||
|
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
|
||||||
|
|
||||||
|
[[Printf]]
|
||||||
|
deps = ["Unicode"]
|
||||||
|
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
|
||||||
|
|
||||||
|
[[Profile]]
|
||||||
|
deps = ["Printf"]
|
||||||
|
uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
|
||||||
|
|
||||||
|
[[REPL]]
|
||||||
|
deps = ["InteractiveUtils", "Markdown", "Sockets"]
|
||||||
|
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
|
||||||
|
|
||||||
|
[[Random]]
|
||||||
|
deps = ["Serialization"]
|
||||||
|
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
|
||||||
|
|
||||||
|
[[Reexport]]
|
||||||
|
deps = ["Pkg"]
|
||||||
|
git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0"
|
||||||
|
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
|
||||||
|
version = "0.2.0"
|
||||||
|
|
||||||
|
[[Requires]]
|
||||||
|
deps = ["UUIDs"]
|
||||||
|
git-tree-sha1 = "d37400976e98018ee840e0ca4f9d20baa231dc6b"
|
||||||
|
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
|
||||||
|
version = "1.0.1"
|
||||||
|
|
||||||
|
[[SHA]]
|
||||||
|
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
|
||||||
|
|
||||||
|
[[Serialization]]
|
||||||
|
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
|
||||||
|
|
||||||
|
[[Sockets]]
|
||||||
|
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
|
||||||
|
|
||||||
|
[[SortingAlgorithms]]
|
||||||
|
deps = ["DataStructures", "Random", "Test"]
|
||||||
|
git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd"
|
||||||
|
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
|
||||||
|
version = "0.3.1"
|
||||||
|
|
||||||
|
[[SparseArrays]]
|
||||||
|
deps = ["LinearAlgebra", "Random"]
|
||||||
|
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
|
||||||
|
|
||||||
|
[[SpecialFunctions]]
|
||||||
|
deps = ["OpenSpecFun_jll"]
|
||||||
|
git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
|
||||||
|
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
|
||||||
|
version = "0.10.3"
|
||||||
|
|
||||||
|
[[StaticArrays]]
|
||||||
|
deps = ["LinearAlgebra", "Random", "Statistics"]
|
||||||
|
git-tree-sha1 = "5c06c0aeb81bef54aed4b3f446847905eb6cbda0"
|
||||||
|
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||||
|
version = "0.12.3"
|
||||||
|
|
||||||
|
[[Statistics]]
|
||||||
|
deps = ["LinearAlgebra", "SparseArrays"]
|
||||||
|
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||||
|
|
||||||
|
[[StatsBase]]
|
||||||
|
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
|
||||||
|
git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
|
||||||
|
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
|
||||||
|
version = "0.33.0"
|
||||||
|
|
||||||
|
[[Test]]
|
||||||
|
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
|
||||||
|
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||||
|
|
||||||
|
[[TimerOutputs]]
|
||||||
|
deps = ["Printf"]
|
||||||
|
git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
|
||||||
|
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
|
||||||
|
version = "0.5.6"
|
||||||
|
|
||||||
|
[[TranscodingStreams]]
|
||||||
|
deps = ["Random", "Test"]
|
||||||
|
git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
|
||||||
|
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
|
||||||
|
version = "0.9.5"
|
||||||
|
|
||||||
|
[[UUIDs]]
|
||||||
|
deps = ["Random", "SHA"]
|
||||||
|
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
|
||||||
|
|
||||||
|
[[Unicode]]
|
||||||
|
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
|
||||||
|
|
||||||
|
[[ZipFile]]
|
||||||
|
deps = ["Libdl", "Printf", "Zlib_jll"]
|
||||||
|
git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
|
||||||
|
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
|
||||||
|
version = "0.9.2"
|
||||||
|
|
||||||
|
[[Zlib_jll]]
|
||||||
|
deps = ["Libdl", "Pkg"]
|
||||||
|
git-tree-sha1 = "622d8b6dc0c7e8029f17127703de9819134d1b71"
|
||||||
|
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
|
||||||
|
version = "1.2.11+14"
|
||||||
|
|
||||||
|
[[Zygote]]
|
||||||
|
deps = ["AbstractFFTs", "ArrayLayouts", "ChainRules", "FillArrays", "ForwardDiff", "Future", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "Random", "Requires", "Statistics", "ZygoteRules"]
|
||||||
|
git-tree-sha1 = "2e2c82549fb0414df10469082fd001e2ede8547c"
|
||||||
|
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
|
||||||
|
version = "0.4.22"
|
||||||
|
|
||||||
|
[[ZygoteRules]]
|
||||||
|
deps = ["MacroTools"]
|
||||||
|
git-tree-sha1 = "b3b4882cc9accf6731a08cc39543fbc6b669dca8"
|
||||||
|
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
|
||||||
|
version = "0.2.0"
|
||||||
|
|
|
||||||
|
|
@ -1,7 +1,12 @@
|
||||||
name = "GreenFlux"
|
name = "GreenFlux"
|
||||||
uuid = "ccad5352-7643-4eb2-b711-e9c298e87bf0"
|
uuid = "ccad5352-7643-4eb2-b711-e9c298e87bf0"
|
||||||
authors = ["Eduardo Cueto Mendoza"]
|
authors = ["Eduardo Cueto Mendoza"]
|
||||||
version = "0.1.0"
|
version = "0.0.4"
|
||||||
|
|
||||||
|
[deps]
|
||||||
|
CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
|
||||||
|
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
|
||||||
|
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||||
|
|
||||||
[compat]
|
[compat]
|
||||||
julia = "1"
|
julia = "1"
|
||||||
|
|
|
||||||
|
|
@ -1,5 +1,24 @@
|
||||||
module GreenFlux
|
module GreenFlux
|
||||||
|
|
||||||
# Write your package code here.
|
using Flux: Chain, Recur, Dense, Conv, MaxPool, GlobalMaxPool, MeanPool, GlobalMeanPool,
|
||||||
|
DepthwiseConv, ConvTranspose, CrossCor, GRUCell, LSTMCell,RNNCell, Maxout
|
||||||
|
using Flux: params, outdims, sigmoid, rrelu, elu, celu, softsign, softplus, tanh, gelu,
|
||||||
|
hardsigmoid, logsigmoid,swish, selu, softmax, logsoftmax, hardtanh,
|
||||||
|
leakyrelu, relu6, lisht, tanhshrink, logcosh, mish, relu, trelu,
|
||||||
|
softshrink, identity
|
||||||
|
using Statistics: mean
|
||||||
|
using CUDAapi: has_cuda_gpu
|
||||||
|
|
||||||
|
export avgpowerdraw, modelflops
|
||||||
|
|
||||||
|
include("power/powerdraw.jl")
|
||||||
|
include("neflops/measureutils.jl")
|
||||||
|
include("neflops/layerflops.jl")
|
||||||
|
include("neflops/gradientflops.jl")
|
||||||
|
include("neflops/modelflops.jl")
|
||||||
|
|
||||||
|
function __init__()
|
||||||
|
@info "Finished loading GreenFlux..."
|
||||||
end
|
end
|
||||||
|
|
||||||
|
end # module
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,206 @@
|
||||||
|
"""
|
||||||
|
gradientflops(layer, input)::Float64
|
||||||
|
|
||||||
|
Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
|
||||||
|
`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
|
||||||
|
`GlobalMaxPool` and `GlobalMeanPool`.
|
||||||
|
|
||||||
|
# Example
|
||||||
|
```julia
|
||||||
|
layer = Conv((2, 2), 1=>16, relu)
|
||||||
|
input = (4,4)
|
||||||
|
layerflops(Conv((2, 2), 1=>16, relu),(4,4))
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
function gradientflops(layer::Dense, input::Tuple)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fn = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
bi = length(layer.b)
|
||||||
|
noofoper = gradientoperations(layer)
|
||||||
|
return convert(Float64, ((xo*yo+bi)*noofoper)*Fm), (xo,yo)
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::Maxout,input::Tuple)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
a,b = layer.over
|
||||||
|
return convert(Float64,xo*yo*Fm), (x,1)
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function gradientflops(layer::Conv, input::Tuple)
|
||||||
|
_,_,_,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
noofoper = gradientoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,xo*yo*noofoper*Fm), (xo,yo,outputfeaturemaps)
|
||||||
|
else
|
||||||
|
return convert(Float64,((xo*yo+size(layer.bias))*noofoper)*Fm), (xo,yo,outputfeaturemaps)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::MaxPool, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey = layer.k
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
return convert(Float64,outx*outy*Fm), (outx,outy)
|
||||||
|
end
|
||||||
|
|
||||||
|
gradientflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
|
||||||
|
|
||||||
|
function gradientflops(layer::MeanPool, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey = layer.k
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
return convert(Float64,outx*outy*Fm), (outx,outy)
|
||||||
|
end
|
||||||
|
|
||||||
|
gradientflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
|
||||||
|
|
||||||
|
function gradientflops(layer::DepthwiseConv, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = gradientoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||||
|
else
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::ConvTranspose, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = gradientoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||||
|
else
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::CrossCor, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
x,y,Fm = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
elseif length(input) == 2
|
||||||
|
x,y,_ = input
|
||||||
|
xo, yo = outdims(layer,(x,y))
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = gradientoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||||
|
else
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
|
||||||
|
inM,inN = input
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper)), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper)), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
|
||||||
|
inM,inN = input
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = 3
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM)), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
|
||||||
|
inM,inN = input
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = 3
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM)), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
@ -0,0 +1,223 @@
|
||||||
|
"""
|
||||||
|
layerflops(layer, input)::Float64
|
||||||
|
|
||||||
|
Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
|
||||||
|
`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
|
||||||
|
`GlobalMaxPool` and `GlobalMeanPool`.
|
||||||
|
|
||||||
|
# Example
|
||||||
|
```julia
|
||||||
|
layer = Conv((2, 2), 1=>16, relu)
|
||||||
|
input = (4,4)
|
||||||
|
layerflops(Conv((2, 2), 1=>16, relu),(4,4))
|
||||||
|
```
|
||||||
|
|
||||||
|
"""
|
||||||
|
function layerflops(layer::Dense,input::Tuple)
|
||||||
|
N,M = size(layer.W)
|
||||||
|
Mi = 0; Ni = 0; Fm = 1; out = 0
|
||||||
|
if length(input) == 3
|
||||||
|
Mi,Ni,Fm = input
|
||||||
|
out = outdims(layer,(Mi,Ni))
|
||||||
|
elseif length(input) == 2
|
||||||
|
Mi,Ni = input
|
||||||
|
Fm = 1
|
||||||
|
out = outdims(layer,input)
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
bi = length(layer.b)
|
||||||
|
if length(out) < 2
|
||||||
|
out = (out[1],1)
|
||||||
|
end
|
||||||
|
noofopers = activationoperations(layer)
|
||||||
|
return convert(Float64,((2*Mi*N - M)+bi)*noofopers*Fm), out
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::Maxout,input::Tuple)
|
||||||
|
i = 0; j = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
i,j,Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
i,j = input
|
||||||
|
Fm = 1
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
a,b = layer.over
|
||||||
|
return convert(Float64,a*b*j*i*Fm), (j,1)
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function layerflops(layer::Conv, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0
|
||||||
|
if length(input) == 3
|
||||||
|
x, y, _ = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
x, y = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
else
|
||||||
|
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::MaxPool, input::Tuple)
|
||||||
|
Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
_,_,Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
_,_ = input
|
||||||
|
Fm = 1
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
kernelsizex,kernelsizey = layer.k
|
||||||
|
outx, outy = outdims(layer,input)
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
|
||||||
|
end
|
||||||
|
|
||||||
|
layerflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
|
||||||
|
|
||||||
|
function layerflops(layer::MeanPool, input::Tuple)
|
||||||
|
Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
_,_,Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
_,_ = input
|
||||||
|
Fm = 1
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
kernelsizex,kernelsizey = layer.k
|
||||||
|
outx, outy = outdims(layer,input)
|
||||||
|
return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
|
||||||
|
end
|
||||||
|
|
||||||
|
layerflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
|
||||||
|
|
||||||
|
function layerflops(layer::DepthwiseConv, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0
|
||||||
|
if length(input) == 3
|
||||||
|
x, y, _ = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
x, y = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
else
|
||||||
|
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::ConvTranspose, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0
|
||||||
|
if length(input) == 3
|
||||||
|
x, y, _ = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
x, y = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
else
|
||||||
|
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::CrossCor, input::Tuple)
|
||||||
|
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||||
|
x = 0; y = 0
|
||||||
|
if length(input) == 3
|
||||||
|
x, y, _ = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
x, y = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
outx, outy = outdims(layer,(x,y))
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.bias) == ()
|
||||||
|
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
else
|
||||||
|
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
|
||||||
|
inM = 0; inN = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
inM,inN, Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
inM,inN = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = activationoperations(layer)
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper))*Fm), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper))*Fm), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
|
||||||
|
inM = 0; inN = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
inM,inN, Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
inM,inN = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = 3
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM))*Fm), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
function layerflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
|
||||||
|
inM = 0; inN = 0; Fm = 1
|
||||||
|
if length(input) == 3
|
||||||
|
inM,inN, Fm = input
|
||||||
|
elseif length(input) == 2
|
||||||
|
inM,inN = input
|
||||||
|
else
|
||||||
|
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||||
|
end
|
||||||
|
WhM,WhN = size(layer.cell.Wh)
|
||||||
|
WiM,WiN = size(layer.cell.Wi)
|
||||||
|
hM = length(layer.cell.h)
|
||||||
|
noofoper = 3
|
||||||
|
if size(layer.cell.b) == ()
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
|
||||||
|
else
|
||||||
|
bM = length(layer.cell.b)
|
||||||
|
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM))*Fm), (bM,1)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
@ -0,0 +1,149 @@
|
||||||
|
"""
|
||||||
|
activationoperations(layer)::Int
|
||||||
|
|
||||||
|
Outputs the approximate mumber of operations for a Flux activation function.
|
||||||
|
|
||||||
|
```julia
|
||||||
|
layer = Conv(weight = weight,
|
||||||
|
σ = sigmoid)
|
||||||
|
|
||||||
|
activationoperations(Conv(weight = weight,
|
||||||
|
σ = sigmoid))
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
function activationoperations(layer::Recur)
|
||||||
|
layer = recurrentunpack(layer)
|
||||||
|
activation = layer.σ
|
||||||
|
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||||
|
activation == softsign || activation == softplus || activation == tanh
|
||||||
|
noofoper = 3
|
||||||
|
elseif activation == gelu
|
||||||
|
noofoper = 8
|
||||||
|
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||||
|
activation == selu || activation == softmax || activation == logsoftmax
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||||
|
activation == lisht || activation == tanhshrink
|
||||||
|
noofoper = 2
|
||||||
|
elseif activation == logcosh || activation == mish
|
||||||
|
noofoper = 5
|
||||||
|
elseif activation == relu || activation == trelu || activation == softshrink
|
||||||
|
noofoper = 1
|
||||||
|
elseif activation == identity
|
||||||
|
noofoper = 0
|
||||||
|
else
|
||||||
|
@info "Unkown activation type defaulting to identity"
|
||||||
|
return 0
|
||||||
|
end
|
||||||
|
return noofoper
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
function activationoperations(layer)
|
||||||
|
activation = layer.σ
|
||||||
|
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||||
|
activation == softsign || activation == softplus || activation == tanh
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == gelu
|
||||||
|
noofoper = 9
|
||||||
|
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||||
|
activation == selu || activation == softmax || activation == logsoftmax
|
||||||
|
noofoper = 5
|
||||||
|
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||||
|
activation == lisht || activation == tanhshrink
|
||||||
|
noofoper = 3
|
||||||
|
elseif activation == logcosh || activation == mish
|
||||||
|
noofoper = 6
|
||||||
|
elseif activation == relu || activation == trelu || activation == softshrink
|
||||||
|
noofoper = 2
|
||||||
|
elseif activation == identity
|
||||||
|
noofoper = 1
|
||||||
|
else
|
||||||
|
@info "Unkown activation type defaulting to identity"
|
||||||
|
return 1
|
||||||
|
end
|
||||||
|
return noofoper
|
||||||
|
end
|
||||||
|
|
||||||
|
"""
|
||||||
|
gradientoperations(layer)::Int
|
||||||
|
|
||||||
|
Outputs the approximate mumber of operations for the gradient of a Flux activation
|
||||||
|
function.
|
||||||
|
|
||||||
|
```julia
|
||||||
|
layer = Conv(weight = weight,
|
||||||
|
σ = sigmoid)
|
||||||
|
|
||||||
|
gradientoperations(Conv(weight = weight,
|
||||||
|
σ = sigmoid))
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
function gradientoperations(layer::Recur)
|
||||||
|
layer = recurrentunpack(layer)
|
||||||
|
activation = layer.σ
|
||||||
|
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||||
|
activation == softsign || activation == softplus || activation == tanh
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == gelu
|
||||||
|
noofoper = 8
|
||||||
|
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||||
|
activation == selu || activation == softmax || activation == logsoftmax
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||||
|
activation == lisht || activation == tanhshrink
|
||||||
|
noofoper = 2
|
||||||
|
elseif activation == logcosh || activation == mish
|
||||||
|
noofoper = 5
|
||||||
|
elseif activation == relu || activation == trelu || activation == softshrink
|
||||||
|
noofoper = 1
|
||||||
|
elseif activation == identity
|
||||||
|
noofoper = 0
|
||||||
|
else
|
||||||
|
@info "Unkown activation type defaulting to identity"
|
||||||
|
return 1
|
||||||
|
end
|
||||||
|
return noofoper
|
||||||
|
end
|
||||||
|
|
||||||
|
function gradientoperations(layer)
|
||||||
|
activation = layer.σ
|
||||||
|
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||||
|
activation == softsign || activation == softplus || activation == tanh
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == gelu
|
||||||
|
noofoper = 8
|
||||||
|
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||||
|
activation == selu || activation == softmax || activation == logsoftmax
|
||||||
|
noofoper = 4
|
||||||
|
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||||
|
activation == lisht || activation == tanhshrink
|
||||||
|
noofoper = 2
|
||||||
|
elseif activation == logcosh || activation == mish
|
||||||
|
noofoper = 5
|
||||||
|
elseif activation == relu || activation == trelu || activation == softshrink
|
||||||
|
noofoper = 1
|
||||||
|
elseif activation == identity
|
||||||
|
noofoper = 0
|
||||||
|
else
|
||||||
|
@info "Unkown activation type defaulting to identity"
|
||||||
|
return 1
|
||||||
|
end
|
||||||
|
return noofoper
|
||||||
|
end
|
||||||
|
|
||||||
|
function recurrentunpack(layer::Recur)
|
||||||
|
return layer.cell
|
||||||
|
end
|
||||||
|
|
||||||
|
islayer(::Any) = false
|
||||||
|
islayer(::Recur) = true
|
||||||
|
islayer(::Dense) = true
|
||||||
|
islayer(::Conv) = true
|
||||||
|
islayer(::MaxPool) = true
|
||||||
|
islayer(::MeanPool) = true
|
||||||
|
islayer(::DepthwiseConv) = true
|
||||||
|
islayer(::ConvTranspose) = true
|
||||||
|
islayer(::CrossCor) = true
|
||||||
|
islayer(::Maxout) = true
|
||||||
|
|
||||||
|
|
@ -0,0 +1,44 @@
|
||||||
|
"""
|
||||||
|
modelflops(model::Chain)::Float64
|
||||||
|
|
||||||
|
Calculates the approximate number of Floating Point Operations that the model will require
|
||||||
|
|
||||||
|
|
||||||
|
```julia
|
||||||
|
weight = rand(Float64, 3, 3, 5)
|
||||||
|
bias = zeros(Float64, 5)
|
||||||
|
Conv(weight = weight,
|
||||||
|
bias = bias,
|
||||||
|
σ = sigmoid)
|
||||||
|
```
|
||||||
|
"""
|
||||||
|
function modelflops(model::Chain,inputsize::Tuple,samplesize::Float64,batchsize::Float64)
|
||||||
|
x = 0; y = 0; Fm = 1
|
||||||
|
if length(inputsize) == 3
|
||||||
|
x,y,Fm = inputsize
|
||||||
|
elseif length(inputsize) == 2
|
||||||
|
x,y = inputsize
|
||||||
|
end
|
||||||
|
modellayers = collect(model)
|
||||||
|
nelayers = Array{Any,1}()
|
||||||
|
layeroutput = Array{Tuple,1}()
|
||||||
|
outsizes = Array{Tuple,1}()
|
||||||
|
lossandgradient = 1
|
||||||
|
for ml in modellayers
|
||||||
|
if islayer(ml)
|
||||||
|
push!(nelayers,ml)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
output = (0,0)
|
||||||
|
for mli in 1:length(nelayers)
|
||||||
|
if mli == 1
|
||||||
|
noflops, output = layerflops(nelayers[mli],inputsize)
|
||||||
|
layeroutput = vcat(layeroutput, noflops)
|
||||||
|
else
|
||||||
|
noflops, output = layerflops(nelayers[mli],output)
|
||||||
|
layeroutput = vcat(layeroutput, noflops)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
numberoflayers = length(layeroutput)
|
||||||
|
return sum(layeroutput) * samplesize + lossandgradient*batchsize
|
||||||
|
end
|
||||||
|
|
@ -0,0 +1,138 @@
|
||||||
|
"""
|
||||||
|
gpupowerdraw()::Float64
|
||||||
|
|
||||||
|
The function uses Linux `nvidia-smi` package to sample and get the average electricity
|
||||||
|
draw of the GPUs.
|
||||||
|
"""
|
||||||
|
function gpupowerdraw()
|
||||||
|
if has_cuda_gpu()
|
||||||
|
gpucommand = `nvidia-smi`
|
||||||
|
usage = Array{Any}(undef,60)
|
||||||
|
cap = Array{Any}(undef,60)
|
||||||
|
nogpus = 0
|
||||||
|
|
||||||
|
for count in 1:60
|
||||||
|
smis = Array{Any}[]
|
||||||
|
smiss = Array{Any}[]
|
||||||
|
gpus = Array{Any}[]
|
||||||
|
powerdraw = Array{Float64}[]
|
||||||
|
powercap = Array{Float64}[]
|
||||||
|
|
||||||
|
smi = read(gpucommand, String);
|
||||||
|
smi = split(smi, "\n")
|
||||||
|
for s in smi
|
||||||
|
push!(smis,split(s, " "))
|
||||||
|
end
|
||||||
|
for s in smis
|
||||||
|
push!(smiss,filter(x->x≠"",s))
|
||||||
|
end
|
||||||
|
for strings in smiss
|
||||||
|
if length(strings) > 5 && strings[6] == "/" && strings[10] == "/"
|
||||||
|
push!(gpus,strings)
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
nogpus = length(gpus)
|
||||||
|
|
||||||
|
for g in gpus
|
||||||
|
usagestr = ""
|
||||||
|
capstr = ""
|
||||||
|
if g[5] == "N/A"
|
||||||
|
usagestr = "0.0"
|
||||||
|
else
|
||||||
|
usagestr = usagestr * g[5]
|
||||||
|
end
|
||||||
|
if g[7] == "N/A"
|
||||||
|
capstr = "0.0"
|
||||||
|
else
|
||||||
|
capstr = capstr * g[7]
|
||||||
|
end
|
||||||
|
powerdraw = vcat(powerdraw, parse(Float64,usagestr))
|
||||||
|
powercap = vcat(powercap, parse(Float64,capstr))
|
||||||
|
end
|
||||||
|
usage[count] = mean(powerdraw)
|
||||||
|
cap[count] = mean(powercap)
|
||||||
|
|
||||||
|
sleep(1)
|
||||||
|
end
|
||||||
|
return nogpus, mean(usage), mean(cap)
|
||||||
|
else
|
||||||
|
@info "This computer does not have acces to a GPU passing to CPU and RAM computations"
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
cpupowerdraw()::Float64
|
||||||
|
|
||||||
|
This function uses the Linux `powerstat` utility to get the average CPU energy cost.
|
||||||
|
"""
|
||||||
|
function cpupowerdraw()
|
||||||
|
cpucommand = `powerstat -R -n -d0`
|
||||||
|
try
|
||||||
|
cpu = read(cpucommand, String);
|
||||||
|
cpu = split(cpu,"\n")
|
||||||
|
cpu = cpu[66][60:64]
|
||||||
|
|
||||||
|
return parse(Float64,cpu)
|
||||||
|
catch e
|
||||||
|
@info "powerstat not installed in your computer"
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
#TODO: further fine tune the model
|
||||||
|
"""
|
||||||
|
rampowerdraw()::Float64
|
||||||
|
|
||||||
|
[Approximate RAM Power Draw](https://www.jedec.org/) the values are provided by the JEDEC we just take the
|
||||||
|
ratio of activated memory against the unactivated for the maximum power value and convert it
|
||||||
|
to hours.
|
||||||
|
"""
|
||||||
|
function rampowerdraw()
|
||||||
|
ramcommand = `free`
|
||||||
|
powerused = Array{Float64}(undef,60)
|
||||||
|
for count in 1:60
|
||||||
|
ram = read(ramcommand, String);
|
||||||
|
ram = split(ram,"\n")
|
||||||
|
ram = split(ram[2]," ")
|
||||||
|
filter!(x->x≠"",ram)
|
||||||
|
usedram = parse(Float64,ram[3])
|
||||||
|
totalram = parse(Float64,ram[2])
|
||||||
|
powerused[count] = ((usedram*1.575)/totalram)*1.904
|
||||||
|
sleep(1)
|
||||||
|
end
|
||||||
|
return mean(powerused)
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
#TODO: modify the code to work in Windows.
|
||||||
|
"""
|
||||||
|
avgpowerdraw()::Float64
|
||||||
|
|
||||||
|
[Average Power Draw](https://arxiv.org/abs/1906.02243) where `pc` is the average power
|
||||||
|
draw (in watts) from all CPU sockets during training, let `pr` be the average power draw from all
|
||||||
|
DRAM (main memory) sockets, let `pg` be the average power draw of the GPUs during training and `g`
|
||||||
|
the number of available gpus.
|
||||||
|
|
||||||
|
`apd = 1.58*t*(pc + pr + g*pg)/1000`
|
||||||
|
|
||||||
|
returns the average power consumption in kWh.
|
||||||
|
"""
|
||||||
|
function avgpowerdraw()
|
||||||
|
if has_cuda_gpu()
|
||||||
|
starttime = time()
|
||||||
|
g, pg, _ = gpupowerdraw()
|
||||||
|
pc = cpupowerdraw()
|
||||||
|
pr = rampowerdraw()
|
||||||
|
endtime = time()
|
||||||
|
elapsedtime = (endtime - starttime)/3600
|
||||||
|
return 1.58*elapsedtime*(pc + pr + g*pg)/1000
|
||||||
|
else
|
||||||
|
pc = cpupowerdraw()
|
||||||
|
pr = rampowerdraw()
|
||||||
|
endtime = time()
|
||||||
|
elapsedtime = (endtime - starttime)/3600
|
||||||
|
return 1.58*elapsedtime*(pc + pr)/1000
|
||||||
|
end
|
||||||
|
end
|
||||||
Loading…
Reference in New Issue